New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods.

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment.

The main study's purpose is to detect novel natural products (NPs) that are potentially selective MAO-B inhibitors and, additionally, to computationally reposition the marketed drugs with a new therapeutic role for Parkinson's disease. To reach the goals, 3D similarity search, docking, ADMETox, and drug repurposing approaches were employed. Thus, an unbiased benchmarking dataset was built including selective and nonselective inhibitors for MAO-B compliant with both ligand- and structure-based virtual screening approaches. A retrospective and prospective mining scenario was applied to SPECS NP and DrugBank databases to detect novel scaffolds with potential benefits for Parkinson's disease patients. Out of the three best selected natural products, cardamomin showed excellently predicted drug-like properties, superior pharmacological profile, and specific interactions with MAO-B active site, indicating a potential selectivity over MAO-B. Two marketed drugs, fenamisal and monobenzone, were proposed as promising candidates repurposed for Parkinson's disease. The application of shape, physicochemical, and electrostatic similarity searches protocol emerged as a plausible solution to explore MAO-B inhibitors selectivity. This protocol might serve as a rewarding tool in early drug discovery and can be extended to other protein targets.

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors.

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure-activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set [Formula: see text]; test set: [Formula: see text]; whole data set: stability [Formula: see text]). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.

COX Inhibition Profile and Molecular Docking Studies of Some 2-(Trimethoxyphenyl)-Thiazoles.

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used therapeutic agents that exhibit frequent and sometimes severe adverse effects, including gastrointestinal ulcerations and cardiovascular disorders. In an effort to obtain safer NSAIDs, we assessed the direct cyclooxygenase (COX) inhibition activity and we investigated the potential COX binding mode of some previously reported 2-(trimethoxyphenyl)-thiazoles. The in vitro COX inhibition assays were performed against ovine COX-1 and human recombinant COX-2. Molecular docking studies were performed to explain the possible interactions between the inhibitors and both COX isoforms binding pockets. Four of the tested compounds proved to be good inhibitors of both COX isoforms, but only compound A3 showed a good COX-2 selectivity index, similar to meloxicam. The plausible binding mode of compound A3 revealed hydrogen bond interactions with binding site key residues including Arg120, Tyr355, Ser530, Met522 and Trp387, whereas hydrophobic contacts were detected with Leu352, Val349, Leu359, Phe518, Gly526, and Ala527. Computationally predicted pharmacokinetic profile revealed A3 as lead candidate. The present data prove that the investigated compounds inhibit COX and thus confirm the previously reported in vivo anti-inflammatory screening results suggesting that A3 is a suitable candidate for further development as a NSAID.

ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes.

Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form the core of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in the Supporting Information and is freely accessible for further studies.

PLS and shape-based similarity analysis of maleimides--GSK-3 inhibitors.

CONTEXT: Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer's disease, cancer, inflammation, obesity, etc. OBJECTIVE: The aim of the current investigation was to model the inhibitory activity of maleimide derivatives--inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure-Activity Relationship (QSAR) and the effect of the template on shape-similarity--binding affinity relationship. MATERIALS AND METHODS: Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated. RESULTS: Reliable statistics R(2)(Y(CUM)) = 0.938/0.920, Q((2)(Y)(CUM)) = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained. DISCUSSION AND CONCLUSIONS: The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

Retrospective group fusion similarity search based on eROCE evaluation metric.

In this study, a simple evaluation metric, denoted as eROCE was proposed to measure the early enrichment of predictive methods. We demonstrated the superior robustness of eROCE compared to other known metrics throughout several active to inactive ratios ranging from 1:10 to 1:1000. Group fusion similarity search was investigated by varying 16 similarity coefficients, five molecular representations (binary and non-binary) and two group fusion rules using two reference structure set sizes. We used a dataset of 3478 actives and 43,938 inactive molecules and the enrichment was analyzed by means of eROCE. This retrospective study provides optimal similarity search parameters in the case of ALDH1A1 inhibitors.

PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) protocol: a brief evaluation.

Docking studies have become popular approaches in drug design, where the binding energy of the ligand in the active site of the protein is estimated by a scoring function. Many promising techniques were developed to enhance the performance of scoring functions including the fusion of multiple scoring functions outcomes into a so-called consensus scoring function. Hereby, we evaluated the target oriented consensus technique using the energetic terms of several scoring functions. The approach was denoted PLSDA-DOCET. Optimization strategies for consensus energetic terms and scoring functions based on ROC metric were compared to classical rigid docking and to ligand-based similarity search methods comprising 2D fingerprints and ROCS. The ROCS results indicate large performance variations depending on the biological target. The AUC-based strategy of PLSDA-DOCET outperformed the other docking approaches regarding simple retrieval and scaffold-hopping. The superior performance of PLSDA-DOCET protocol relative to single and combined scoring functions was validated on an external test set. We found a relative low mean correlation of the ranks of the chemotypes retrieved by the PLSDA-DOCET protocol and all the other methods employed here.

Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors.

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.

Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors.

The current work was conducted to investigate the effectiveness of two conceptually distinct in silico ligand-based tools: Partial Least Squares Discriminant Analysis (PLS-DA) and 3D similarity, including shape, physico-chemical and electrostatics to classify target-specific pharmacophores with enrichment power for selective GSK-3 inhibitors against the phylogenetically related CDK-2, CDK-4, CDK-5 and PKC. All virtual screens were performed on four data sets of targets matched pairwise, including selective and nonselective inhibitors for GSK-3. The classification method PLS-DA results revealed that all obtained models are statistically robust according to the cross-validation and response permutation tests. Regarding selective GSK-3 inhibitors differentiation in terms of selectivity (Se), specificity (Sp), and accuracy (ACC), the PLS-DA models for CDK-4/GSK-3, and PKC/GSK-3 datasets are highly efficient discriminative. 3D similarity searches for CDK-4/GSK-3, PKC/GSK-3, and CDK-2/GSK-3 datasets using the most selective reference molecules lead to highest enrichments of selective GSK-3 inhibitors. EON yields excellent early and overall enrichments for ET_ST and ET_combo for most selective query for CDK-4/GSK-3. CDK-5/GSK-3 dataset didn't show consistent statistically significant enrichments for 3D similarity virtual screening. The current methodology is reliable and could be used as a powerful tool for the detection of potentially selective molecules targeting GSK-3.

More effective DPP4 inhibitors as antidiabetics based on sitagliptin applied QSAR and clinical methods.

Xanthine-based molecules such as serine protease dipeptidyl peptidase 4 (DPP4) inhibitors are compounds often used in improving glycemic control in type 2 diabetic patients and also used for their effects as mild stimulants and as bronchodilators, notably in treating asthma symptoms. Here, we aim to better understand the molecular features affecting activity of xanthine-based DPP4 inhibitors such as sitagliptin and related compounds and use these features to de novo predict improved sitagliptin derivatives. To this end, we performed a clinical study to examine the efficacy and safety of once-daily 100 mg oral sitagliptin as monotherapy in Romanian patients with type 2 diabetes. This study indicates that sitagliptin effectively decreases the glycemic level and provides very good glycemic equilibrium. To predict putative new drugs with identical pharmacological effects at lower dosages, we generate QSAR models based on compound series containing 35 DPP4 inhibitors. We establish that the physicochemical parameters critical for DPP4 inhibitory activity are: hydrophobicity described by the logarithm of the octanol/water partition coefficient, counts of rotatable bonds, hydrogen bond donor and acceptor atoms, and topological polar surface area. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q2 (0.77), fitted correlation coefficient r2 (0.85) and standard error of prediction (0.34). Based on the established QSAR equations, we propose and analyse 19 new sitagliptin derivatives with possibly improved pharmacological effect as DPP4 inhibitors.

QSPR modeling of hyperpolarizabilities.

The polarizabilities and the first and second hyperpolarizabilities of 219 conjugated organic compounds are modeled by QSPR (quantitative structure activity relationship) based on a large pool of constitutional, topological, electronic and quantum chemical descriptors calculated by CODESSA Pro (comprehensive descriptors for structural and statistical analysis) derived solely from molecular structure. Multilinear models were developed using the BMLR (best multilinear regression) algorithm to relate the experimental (hyper)polarizabilities to their predicted values. The regression equations include AM1 (Austin model 1) calculated (hyper)polarizabilities together with the size, electrostatic and quantum chemical descriptors to compensate for the imprecision of the AM1 computational method. The results emphasize the main factors that influence (hyper)polarizability. All models were validated by the "leave-one-out" method and internal validations that confirmed the stability and good predictive ability.

QSPR study of critical micelle concentration of anionic surfactants using computational molecular descriptors.

A data set of 181 diverse anionic surfactants has been investigated to relate the logarithm of critical micelle concentration (cmc) to the molecular structure using Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA Pro) software. A fragment approach provided superior quantitative structure-property relationship (QSPR) models in terms of statistical characteristics and predictive ability. The regression equations provided insight into the structural features of surfactants that influence cmc. The most obvious influence on cmc was manifested by hydrophobic fragments expressed by the topological and geometrical descriptors, while the hydrophilic fragment is represented by constitutional, geometrical, and charge related descriptors. Significantly important molecular descriptors in the selected QSPR models were topological, solvational, and charge-related descriptors as the driving force of the intermolecular interactions between anionic surfactants and water.

QSAR study of antiplatelet agents.

A QSAR methodology that involves multilinear (Hansch-type) and nonlinear (ANN backpropagation) approaches was developed to correlate the antiplatelet activity of 60 benzoxazinone derivatives against factor Xa. The statistical characteristics provided by multilinear model (R2 = 0.821) indicated satisfactory stability and predictive ability, while the ANN predictive ability is somewhat superior (R2 = 0.909). The multilinear model provided insight into the main factors that modulate the inhibitory activity of the investigated compounds.

QSAR modeling of the inhibition of glycogen synthase kinase-3.

Quantitative structure-activity relationship (QSAR) models of the biological activity (pIC50) of 277 inhibitors of Glycogen Synthase Kinase-3 (GSK-3) are developed using geometrical, topological, quantum mechanical, and electronic descriptors calculated by CODESSA PRO. The linear (multilinear regression) and nonlinear (artificial neural network) models obtained link the structures to their reported activity pIC50. The results are discussed in the light of the main factors that influence the inhibitory activity of the GSK-3 enzyme.